EART30392
Geostatistics and Advanced Reservoir Modelling
10
Semester 2
Undergraduate
Level 6
Rufus Brunt
Unit coordinator
Department of Earth and Environmental Sciences
1
FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors '
European Credit Transfer & Accumulation System Rating : 5.0
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<p><strong>Theory:</strong></p><p> 1. What is a Reservoir Model and why do we need them in the petroleum industry.</p><p> 2. Markov Chains and Monte-Carlo simulations.</p><p> 3. Univariate and Multivariate statistics applied to reservoir characterisation.</p><p> 4. Reservoir modelling algorithms: How they work and where to apply them.</p><p><strong>Practical:</strong></p><p> Getting started:</p><p> 1. Introduction to the Petrel Reservoir Modelling package.</p><p> 2. Structural modelling: modelling faults and horizons</p><p> <strong>Modelling algorithms by example:</strong></p><p> 1. Deepwater fan complex: Sequential Indicator Simulations (SIS)</p><p> 2. Confined fluvial systems: Object Modelling</p><p> 3. Shoreface settings: Truncated Gaussian Simulations</p><p> 4. Carbonate reservoirs: SIS, Probability volumes, Discrete Fracture Networks.</p><p> This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates.</p><p> </p><p> </p></div>
<p> </p>
<p><strong><strong>Theory:</strong></strong></p><p><strong> 1. What is a Reservoir Model and why do we need them in the petroleum industry.</strong></p><p><strong> 2. Markov Chains and Monte-Carlo simulations.</strong></p><p><strong> 3. Univariate and Multivariate statistics applied to reservoir characterisation.</strong></p><p><strong> 4. Reservoir modelling algorithms: How they work and where to apply them.</strong></p><p><strong><strong>Practical:</strong></strong></p><p><strong> Getting started:</strong></p><p><strong> 1. Introduction to the Petrel Reservoir Modelling package.</strong></p><p><strong> 2. Structural modelling: modelling faults and horizons</strong></p><p><strong> <strong>Modelling algorithms by example:</strong></strong></p><p><strong> 1. Deepwater fan complex: Sequential Indicator Simulations (SIS)</strong></p><p><strong> 2. Confined fluvial systems: Object Modelling</strong></p><p><strong> 3. Shoreface settings: Truncated Gaussian Simulations</strong></p><p><strong> 4. Carbonate reservoirs: SIS, Probability volumes, Discrete Fracture Networks.</strong></p><p><strong>This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates.</strong></p><p> </p>
<div> <p><em>The unit aims to:</em></p><p>[i] Introduce the principles of Stochastic Reservoir Modelling, and the underlying statistics that support this modelling approach.</p><p>[ii] Introduce the concepts of uncertainty and risk, and how those concepts apply to reservoir modelling.</p><p>[iii] Develop key skills and attributes of those seeking employment in the petroleum geology sector.</p></div><p> </p>
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<p><strong><em>On the successful completion of the course, students will be able to: </em></strong></p></td>
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<p><strong>Developed</strong></p></td>
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<p><strong>Assessed</strong></p></td>
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<p><strong>ILO 1</strong></p></td>
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<p><strong>Explain what a reservoir model is, and why building reservoir models is important.</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p><strong>ILO 2</strong></p></td>
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<p><strong>Summarize the steps involved in running Monte-Carlo simulations, and explain why Monte Carlo simulations are used for reservoir modelling.</strong></p><p><strong> </strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p><strong>ILO 3</strong></p></td>
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<p><strong>Describe the main reservoir modelling algorithms, and appraise their effectiveness in modelling different depositional environments.</strong></p><p><strong> </strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p><strong>ILO 4</strong></p></td>
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<p><strong>Explain what uncertainty is, and why it is of such importance in the petroleum industry.</strong></p><p><strong> </strong></p><p><strong> </strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p><strong>ILO 5</strong></p></td>
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<p><strong>Reduce a complex reservoir modelling problem into a series of smaller, more achievable problems.</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p align="center"><strong>Y</strong></p></td>
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<p>[1] On-line lectures and associated course manual.</p>
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<p>[2] Practicals delivered as on-line training videos with associated formative tests.</p><p>[3] Directed and independent reading</p><p>[4] Directed and independent exercises</p><p>[5] Discussions</p><p>[6] Feedback – use of on-line test allow instantaneous feedback on progress.</p><p> </p><p>This unit will be delivered as a 1-week block course by an external lecturer at the start of semester 2.</p></div>
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0
Other
50%
9
Set exercise
50%
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<p align="center">Assessment type</p></td>
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<p>% Weighting within unit</p></td>
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<p align="center">Hand out and hand in dates</p></td>
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<p align="center">Length</p><p align="center"> </p></td>
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<p align="center">How, when and what feedback is provided</p></td>
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<p>ILO tested</p></td>
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<p>Formative Assessment: Brief on-line multiple choice questions</p></td>
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<p>0</p></td>
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<p>after each practical assignment</p></td>
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<p>1.5 hrs</p></td>
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<p>Immediate via online test, and discussion in class</p></td>
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<p>all</p></td>
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<p>2 Online tests</p></td>
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<p>2x50</p></td>
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<p>Weeks 7 and 11</p></td>
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<p>1 hr each</p></td>
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<p>On-line feedback within 2 working days.</p></td>
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<p>all</p></td>
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B.Eng in Petroleum Engineering
B.Eng in Petroleum Engineering
Third Year
Mandatory
M.Eng in Petroleum Engineering
M.Eng in Petroleum Engineering
Third Year
Mandatory
Y
<p>Caers, J. & Society of Petroleum, E. 2005. Petroleum geostatistics. Society of Petroleum Engineers, Richardson, Tex.</p><p>Coburn, T. C., Yarus, J. M., Chambers, R. L. & American Association of Petroleum, G. 2006. Stochastic modeling and geostatistics : principles, methods, and case studies, volume II. American Association of Petroleum Geologists, Tulsa, Okla.</p><p>Deutsch, C. V. 2002. Geostatistical reservoir modeling. Oxford University Press, Oxford ; New York.</p><p>Grammer, G. M., Harris, P. M., Eberli, G. P. & American Association of Petroleum, G. 2004. Integration of outcrop and modern analogs in reservoir modeling. American Association of Petroleum Geologists, Tulsa, Okla.</p><p>Hodgetts. D., Drinkwater, N.J., Hodgson, D.M., Kavanagh, J., Flint, S., Keogh, K., & Howell, J.. (2004) Three-dimensional geological models from outcrop data using digital data collection techniques: an example from the Tanqua Karoo depocentre, South Africa. In Geological Prior Information: Informing Science and Engineering. Geological Society Special Publication 239 (ed. A. Curtis and R. Woods), pp. 57-75. Geological Society</p><p>Ringrose, P. & Bentley, M (2015) Reservoir Model Design: A Practitioner's Guide. ISBN: 978-94-007-5496-6 (Print) 978-94-007-5497-3 (Online): Springer</p><p>Yarus, J. M. and Chambers, R. L. 1994. Stochastic modeling and geostatistics: Principles, methods and case studies AAPG computer applications in geology No 3: American Association of Petroleum Geologists, Tulsa, Oklahoma, 1994, 379 pp., ISBN 0-89181-702-6</p><p>Ringrose, P & Bentley, M. 2015. Reservoir Model Design: A Practitioner's Guide. Springer.</p>
Practical classes & workshops
30
0
70